DBMS > Snowflake vs. VelocityDB vs. Vertica vs. Vitess

System Properties Comparison Snowflake vs. VelocityDB vs. Vertica vs. Vitess

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Editorial information provided by DB-Engines

Name

Description

Cloud-based data warehousing service for structured and semi-structured data

A .NET Object Database that can be embedded/distributed and extended to a graph data model (VelocityGraph)

Cloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.

Scalable, distributed, cloud-native DBMS, extending MySQL

Primary database model

Relational DBMS

Graph DBMS
Object oriented DBMS

Relational DBMS

Column oriented

Relational DBMS

Secondary database models

Spatial DBMS
Time Series DBMS

Document store
Spatial DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	133.72
Rank	#7	Overall
	#5	Relational DBMS

Score	0.00
Rank	#385	Overall
	#40	Graph DBMS
	#21	Object oriented DBMS

Score	9.62
Rank	#42	Overall
	#26	Relational DBMS

Score	0.86
Rank	#202	Overall
	#95	Relational DBMS

Website

Technical documentation

docs.snowflake.net/manuals/index.html

velocitydb.com/UserGuide

vertica.com/documentation

vitess.io/docs

Developer

Snowflake Computing Inc.

VelocityDB Inc

OpenText

previously Micro Focus and Hewlett Packard

The Linux Foundation, PlanetScale

Initial release

2014

2011

2005

2013

Current release

7.x

12.0.3, January 2023

15.0.2, December 2022

License

Commercial or Open Source

commercial

Limited community edition free

Open Source

Apache Version 2.0, commercial licenses available

Cloud-based only

Only available as a cloud service

yes

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

Server operating systems

hosted

Any that supports .NET

Linux

Docker
Linux
macOS

Data scheme

yes

support of semi-structured data formats (JSON, XML, Avro)

yes

Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.

yes

Typing

predefined data types such as float or date

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

yes

Secondary indexes

yes

No Indexes Required. Different internal optimization strategy, but same functionality included.

yes

SQL

Support of SQL

yes

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

yes

with proprietary extensions

APIs and other access methods

CLI Client
JDBC
ODBC

.Net

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

ADO.NET
JDBC
MySQL protocol
ODBC

Supported programming languages

JavaScript (Node.js)
Python

.Net

C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

user defined functions

yes, PostgreSQL PL/pgSQL, with minor differences

yes

proprietary syntax

Triggers

similar concept for controling cloud resources

Callbacks are triggered when data changes

yes, called Custom Alerts

yes

Partitioning methods

Methods for storing different data on different nodes

yes

Sharding

horizontal partitioning, hierarchical partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Bi-directional Spark integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency across shards
Immediate Consistency within a shard

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

ACID at shard level

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

Durability

Support for making data persistent

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

Users with fine-grained authorization concept, user roles and pluggable authentication

Based on Windows Authentication

fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

Users with fine-grained authorization concept

no user groups or roles

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